10 Use Cases for AI in HR (with actual examples) - Part 2

A further exploration of use cases, including Skills Management, Workforce Planning and others.

Recap of Part 1

In Part 1 of this article, we discussed use cases of AI in HR, focusing around:

• Talent Acquisition

• Talent Development

• Employee Benefits

• Total Rewards

• HR Operations

In the second part of this article, I will focus on a few other applications, some of which are quite mature, like Workforce Planning, and others that are a bit more novel, such as Employee Performance Management, and some that are really thriving like Skills Management.

The latter, in particular, is at the core of the concept of a Skills-based organization, which aims to deconstruct the traditional notion of job descriptions and help identify and develop talent from within the organization. This approach has a lot of potential in the quest for making talent availability more predictable, building it on-demand, and even disrupting some of the existing Talent Acquisition bias, hiring for skills rather than for “candidate pedigree”. But that’s a topic for a different post…

Skills Management:

• Skills assessment and gap analysis: AI-powered platforms can analyze employee skills, certifications, and job performance data to assess individual skill levels and identify skill gaps. This information can be used to create personalized development plans and training recommendations.
  Examples: Gloat, Retrain.ai.

• Skills mapping and alignment: AI algorithms can analyze job descriptions, competency frameworks, and employee profiles to map relevant skills required for different roles. This enables HR to identify potential skill overlaps, succession planning opportunities, and areas where upskilling or reskilling may be needed.
  Examples: Gloat, Augmentir, and Glint (which is now integrated into the Microsoft Viva Employee Experience platform).

• Talent pipeline identification: AI-powered systems can identify potential internal candidates for key positions based on skills, performance, and career progression, aiding in succession planning and reducing talent shortages.
  Example: SanaLabs.

Workforce Planning:

• Demand forecasting: AI algorithms can analyze historical data, market trends, and business projections to forecast future workforce demand by role, location, or department. This assists HR in aligning talent acquisition and workforce strategies with anticipated needs and succession planning. Examples: IBM Planning Analytics, Augmentir

• Scenario planning and talent optimization: AI can simulate different scenarios, such as business expansion, downsizing, or restructuring, and provide recommendations on workforce allocation, talent redeployment, and skill development strategies to optimize workforce composition.
  Examples: Faethm, Lightcast, Quinyx.

Employee Performance Management:

• Performance analytics: AI can analyze performance data, feedback, and other relevant factors to provide insights on employee performance trends, strengths, and areas for improvement.
  Examples: Effy, Jive, PeopleHum.

• Predictive performance modeling: AI algorithms can predict future performance based on historical data, enabling HR to identify high-potential employees and allocate resources effectively.
  Examples: Trakstar, Visier, Akkio.

• Real-time feedback and coaching: AI-powered tools can provide real-time feedback and coaching suggestions to managers and employees during performance discussions, fostering continuous improvement.
  Examples: Engagedly, BetterUp, BetterWorks.

Employee Engagement and Sentiment Analysis:

• Pulse surveys and sentiment analysis: AI can analyze employee survey responses, sentiment in employee feedback, and social media data to gauge employee satisfaction and engagement, allowing the identification of potential issues or areas for improvement.
  Examples: Lattice, Visier, Simpplr.

• Employee sentiment monitoring: AI tools can monitor employee communication channels, such as emails and chat platforms, to identify sentiment trends and detect signs of disengagement or potential conflicts. Examples: Erudit, Microsoft Viva Insights.

Compliance and Risk Management:

• Bias detection and mitigation in hiring: AI can analyze recruitment data to identify potential biases in the hiring process, such as gender or racial bias, helping HR teams promote fairness and diversity.
  Examples: Fountain, Hally.ai.

• Compliance monitoring: AI can analyze HR policies, employee data, and regulatory requirements to identify compliance risks and provide recommendations for ensuring adherence to labor laws, privacy regulations, and other compliance standards.
  Examples: AGAT.

Some closing remarks

1. AI is a polarizing topic, and while some affirm that it can reduce bias, others argue that it can make things worse because LLMs are trained by humans with data generated by other humans. As with everything AI, the rule is to use it as your copilot, rather than abdicating your responsibilities to it and to educate yourself to better use this tool. Think of it as the sharpest power tool in your workshop: it can make your work SO much faster and more efficient, but it can also cause a lot of harm if used carelessly or by the untrained.

2. A whole corpus of knowledge is being built around AI ethics. Best practices keep evolving and will definitely impact the HR practice and where and how to use AI. Some authors even argue that HR should lead the charge and own You should be wary of just running after the latest technology fad. Make sure you have AI usage policies that protect your people and your organization and stay vigilant about the ethical use of AI in your organization.

3. New tools are being launched every day, and this article is probably going to become obsolete pretty fast, as more and more AI applications become available. The best way to keep up with the latest developments is to follow sites like Product Hunt for new launches and to periodically check directories like Futurepedia, AIToolsDirectory, or Insidr. You can also follow industry thought leaders like Josh Bersin, Laszlo Bock, David Green, Kathleen Hogan, Nicolas Behbahani, Dave Ulrich, or Bridgette Hyacinth.

And, of course, you can follow ProductizeHR on Substack, and follow me, Hernan Chiosso, on LinkedIn.

Do you enjoy reading this type of article? Should I follow up with a more in-depth look at some of the tools? Let me know in the comments! 👂👂👂